Identifying interpretable high-order feature interactions is important in both machine learning and data visualization, specially for biomedical applications. In this paper, we propose a new model called Sparse High-order Boltzmann Machine (SHBM) to identify interpretable high-order feature interactions in an unsupervised setting. The learning for SHBM is decoupled into two steps: interaction n...

Big data is a collection of data sets which is used to describe the exponential growth and availability of both ordered and amorphous data. It is difficult to process big data using traditional data processing applications. In many practical problems, deep learning is one of the machine learning algorithms that has received great popularity in both academia and industry due to its high-level ab...

The Boltzmann Machine (BM) is a neural network composed of stochastically firing neurons that can learn complex probability distributions by adapting the synaptic interactions between neurons. BMs represent very generic class stochastic networks be used for data clustering, generative modelling and deep learning. A key drawback software-based required Monte Carlo sampling, which scales intracta...

We present a fully automated generative method for simultaneous brain tumor and organs-at-risk segmentation in multi-modal magnetic resonance images. The method combines an existing whole-brain segmentation technique with a spatial tumor prior, which uses convolutional restricted Boltzmann machines to model tumor shape. The method is not tuned to any specific imaging protocol and can simultaneo...

The partition function for a Boltzmann machine can be bounded from above and below. We can use this to bound the means and the correlations. For networks with small weights, the values of these statistics can be restricted to non-trivial regions (i.e. a subset of [-1 , 1]). Experimental results show that reasonable bounding occurs for weight sizes where mean field expansions generally give good...

In this paper we show that given the set of positive and negative examples of a concept it is possible to build a Boltzmann Machine that aproximates the minimum Disjunctive Normal Form formula consistent with examples.

Scene models allow robots to reason about what is in the scene, what else should be in it, and what should not be in it. In this paper, we propose a hybrid Boltzmann Machine (BM) for scene modeling where relations between objects are integrated. To be able to do that, we extend BM to include tri-way edges between visible (object) nodes and make the network to share the relations across differen...

The main areas of work being covered by the Work Stream are: 1. Developing testing guidelines for new VC paradigms: • The Work Stream is working closely with (External Scientific Advisory Committee) ESAC3 of IVCC and WHO on issues related to the development of new vector control interventions, including draft testing guidelines. • The Work Stream aims to develop with IVCC a similar working rela...